Cross-Selling of Credit Cards

Author: Nils Fahrni

import warnings
warnings.filterwarnings("ignore")

import sys
import os

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import plotly.graph_objects as go
import plotly.io as pio
import seaborn as sns

plt.style.use('ggplot')

pio.templates.default = 'ggplot2'
pio.renderers.default = "notebook"
pd.set_option('display.max_columns', None)

current_dir = os.getcwd()
parent_dir = os.path.dirname(current_dir)

np.random.seed(1337)

sys.path.append(parent_dir)

Data Overview

Data Preprocessing

In the following section the goal is to load in all artifacts from the PKDD’99 Dataset and to merge them together into a single dataframe that will allow for further processing steps.

Data Loading

To load the PKDD’99 Dataset, a DataLoader class was created to streamline the onloading process. The dataset already has a few structural flaws, such as the dates being represented as YYMMDD, so just a single integer number. The DataLoader object takes care of this flaw and imports all date columns in actual datetype formats. This will later on allow for simpler date-specific operations and interpretation.

Another task that the DataLoader takes on is the translation. The PKDD’99 Dataset stems from a Czech bank which means that a lot of column names and categorical values are in Czech language. To tranlsate these columns and their values into English, a translation_mappings.json file inside the /data directory was created. This JSON file helps to control which values and attributes need to get translated and their respective new name. The file can be passed into the DataLoader, resulting in new translated names among all variables.

from src.data_utils import DataLoader

data_loader = DataLoader(base_path='data', translations_name='translation_mappings.json')
data_loader.list_datasets()

	Dataset	Number of Rows
0	loan	682
1	client	5369
2	district	77
3	trans	1056320
4	account	4500
5	card	892
6	order	6471
7	disp	5369

To make sure everything will get loaded correctly, the instantiated data_loader object has a list_datasets() method which yields a glimpse of the .csv-files inside the /data directory.

Account

First, the Accounts will get loaded in. As can be seen in the Entity Relationship Diagram, this table is the centerpiece of all the relations inside the dataset. As already touched upon, the date variables are represented as 6-digit integers inside the .csv files. This will get addressed during the loading process. For this a date parsing pattern gets passed into the function so that we can interpret the unformatted parts of the integers and convert them into the corresponding time piece (year, month and day).

account = data_loader.load_csv('account', parse_dates={'date': '%y%m%d'})
account.sample(5)

Mapped frequency:
{
    "POPLATEK MESICNE": "MONTHLY CHARGES",
    "POPLATEK TYDNE": "WEEKLY CHARGES",
    "POPLATEK PO OBRATU": "TRANSACTION CHARGES"
}

	account_id	district_id	frequency	date
2103	3646	63	WEEKLY CHARGES	1995-10-20
1721	981	66	MONTHLY CHARGES	1995-03-25
1193	2501	30	MONTHLY CHARGES	1994-02-26
2892	2604	68	MONTHLY CHARGES	1996-07-04
252	1189	59	MONTHLY CHARGES	1993-03-19

The sample shows that the translation mappings also got correctly applied, the frequency column was translated correctly into english.

Client

The next table is the client .csv. It also has multiple relations: Once to the Dispositions and once to the Districts.

Inside the client table there also was a special remark: The birth_number variable has the sex of each client encoded. The male “birth numbers” are just date integers as we already noticed in the first section. The female birth numbers however have an added 50 days on each date integer. Since sex/gender could also be of value to us later on, the birthdates get “fixed” by turning them both into regular datetime values. Additionally the gender will be written into another column based on if the day-part of the date integer is greater or lower than 50. The birthdate also allows us to directly extract the age of the clients in year, so an age column gets added by subtracting the birthdate from a proposed “key date” which is 1999-12-31. Of course in a real-world scenario this date would be the current date and not some arbituary past date.

client = data_loader.load_csv('client')

client = client.assign(gender=client['birth_number'].apply(lambda x: 'FEMALE' if int(str(x)[2:4]) > 50 else 'MALE'))

client = client.assign(birth_number=client.apply(lambda x: x['birth_number'] - 5000 if x['gender'] == 'FEMALE' else x['birth_number'], axis=1))

client['birth_number'] = pd.to_datetime(client['birth_number'], format='%y%m%d')
client['birth_date'] = client['birth_number'].apply(lambda x: x - pd.DateOffset(years=100) if x.year > 1999 else x)
client.drop('birth_number', axis=1, inplace=True)

client['age'] = (pd.to_datetime('1999-12-31') - client['birth_date']).dt.days // 365

client.sample(5)

	client_id	district_id	gender	birth_date	age
4105	4336	19	FEMALE	1969-08-02	30
2112	2229	68	MALE	1944-09-07	55
1463	1547	28	FEMALE	1927-12-20	72
3951	4177	34	MALE	1935-12-25	64
3835	4052	74	FEMALE	1950-10-17	49

The sample shows that the dates got formatted correctly and our two new columns (gender and age) have been added correctly. Within this table no translations were needed, hence no translation-mapping output.

Disposition

The disposition table holds information on which clients have the “right” to issue permanent orders and ask for a loan. The owners of each account are marked as “OWNER”. Other users relations to an account are marked as “DISPONENT”.

It is assumed that only account owners would have the right to make a permanent order like buying a new card, hence the decision was made to remove non-owners (disponents). Disponents may be secondary users that have been authorized to use an account. They may be allowed to execute transactions on that account but they are not the authorized owners.

disp = data_loader.load_csv('disp')

disp = disp[disp['type'] != 'DISPONENT']

disp.drop('type', axis=1, inplace=True)

disp.sample(5)

	disp_id	client_id	account_id
4478	4737	4737	3928
5044	9512	9820	7944
3781	3994	3994	3302
1450	1531	1531	1269
1439	1520	1520	1259

Permanent Order

A permanent order record describes an order made between two accounts; The account_id (sender) sends amount to account_to (beneficiary) at bank bank_to. Each can also be marked with a k_symbol (type of payment).

order = data_loader.load_csv('order')

order.sample(5)

Mapped k_symbol:
{
    "POJISTNE": "INSURANCE PAYMENT",
    "SIPO": "HOUSEHOLD",
    "LEASING": "LEASING",
    "UVER": "LOAN PAYMENT"
}

	order_id	account_id	bank_to	account_to	amount	k_symbol
5126	35080	3854	QR	36950737	1290.0	HOUSEHOLD
2680	32349	2016	ST	81232386	157.0	INSURANCE PAYMENT
403	29843	291	YZ	27026367	12925.0	NaN
5868	40382	7437	EF	2692229	6901.0	HOUSEHOLD
5952	41067	7861	OP	58806037	400.0	NaN

Here, the sample shows that the k_symbol column got translated correctly.

It seems that not every order has a labeled type of payment. To validate this observation we will take a closer look at orders with an undefined k_symbol.

display(order.isnull().sum())

display(order[order['k_symbol'].isnull()].sample(5))

order_id         0
account_id       0
bank_to          0
account_to       0
amount           0
k_symbol      1379
dtype: int64

	order_id	account_id	bank_to	account_to	amount	k_symbol
2746	32417	2057	YZ	1773274	970.0	NaN
69	29475	48	ST	2071685	1029.0	NaN
6420	45778	11013	IJ	43103534	9499.0	NaN
1788	31375	1342	ST	45400208	1011.0	NaN
2847	32533	2139	GH	92438989	4737.0	NaN

The other variables do not show additional abnormality so it can be concluded that the k_symbol is not a mandatory field for every order.

To still keep the information of these undefined orders a “MISSING” label will get added.

The orders contain granular information on which account forwarded money to another bank/recipient. To build a client summary at the end these orders need to get compressed into valuable information which can be assigned to a client - Therefore, the k_symbol gets pivoted and for each order a count is added to the respective order type.

order['k_symbol'] = order['k_symbol'].fillna('MISSING')

order_pivot = order.pivot_table(index='account_id', columns='k_symbol', values='amount', aggfunc='count', fill_value=0)

order_pivot.sample(5)

k_symbol	HOUSEHOLD	INSURANCE PAYMENT	LEASING	LOAN PAYMENT	MISSING
account_id
552	1	0	0	0	0
198	2	0	0	0	0
578	1	1	0	0	2
1583	0	0	0	1	0
226	1	0	0	1	0

Transaction

The transactions seem to be fairly similar to the orders. They also get categorized by a k_symbol attribute. This category’s values should therefore also be translated.

transaction = data_loader.load_csv('trans', parse_dates={'date': '%y%m%d'})

transaction.sample(5)

Mapped type:
{
    "PRIJEM": "CREDIT",
    "VYDAJ": "WITHDRAWAL"
}
Mapped operation:
{
    "VYBER KARTOU": "CREDIT CARD WITHDRAWAL",
    "VKLAD": "CREDIT IN CASH",
    "PREVOD Z UCTU": "COLLECTION FROM ANOTHER BANK",
    "VYBER": "WITHDRAWAL IN CASH",
    "PREVOD NA UCET": "REMITTANCE TO ANOTHER BANK"
}
Mapped k_symbol:
{
    "POJISTNE": "INSURANCE PAYMENT",
    "SLUZBY": "PAYMENT FOR STATEMENT",
    "UROK": "INTEREST CREDITED",
    "SANKC. UROK": "SANCTION INTEREST IF NEGATIVE BALANCE",
    "SIPO": "HOUSEHOLD",
    "DUCHOD": "OLD-AGE PENSION",
    "UVER": "LOAN PAYMENT"
}

	trans_id	account_id	date	type	operation	amount	balance	k_symbol	bank	account
926595	3444421	3223	1998-07-31	CREDIT	NaN	92.9	14640.3	INTEREST CREDITED	NaN	NaN
715788	134286	450	1997-12-12	CREDIT	COLLECTION FROM ANOTHER BANK	4393.0	16076.3	OLD-AGE PENSION	AB	94329887.0
673209	3564940	1046	1997-10-31	CREDIT	NaN	130.1	27631.5	INTEREST CREDITED	NaN	NaN
776368	204567	694	1998-01-31	WITHDRAWAL	WITHDRAWAL IN CASH	14.6	29151.1	PAYMENT FOR STATEMENT	NaN	NaN
1028116	3600996	2141	1998-11-30	CREDIT	NaN	283.4	70926.4	INTEREST CREDITED	NaN	NaN

Loan

The loan table holds records of the loans given out to certain accounts. It contains the loan amount, the duration of it in months, the monthly interest (payments) and a status.

The status is a categorical variable which gets mapped to some clearer status descriptions.

loan = data_loader.load_csv('loan', parse_dates={'date': '%y%m%d'})

loan.sample(5)

Mapped status:
{
    "A": "contract finished, no problems",
    "B": "contract finished, loan not payed",
    "C": "running contract, OK so far",
    "D": "running contract, client in debt"
}

	loan_id	account_id	date	amount	duration	payments	status
629	5015	226	1998-07-12	109344	12	9112.0	running contract, OK so far
323	6818	9030	1996-12-12	155616	48	3242.0	running contract, OK so far
670	6168	5698	1998-11-21	99216	36	2756.0	running contract, OK so far
407	6278	6265	1997-06-10	87360	24	3640.0	running contract, OK so far
27	5189	1166	1994-02-07	149040	48	3105.0	contract finished, no problems

According to the Entity Relationship Diagram, an account can in theory have multiple loans. If an account has multiple loans that would make the preprocessing a bit harder since we cannot match a loan 1:1 into a summarizing table. Therefore, a first check would be to look if there are multiple loans for a given account:

print(f'Are there accounts with multiple loans: {loan["account_id"].nunique() < loan.shape[0]}')

Are there accounts with multiple loans: False

The check yielded that there are only unique accounts for all loans, so no account has more than one loan.

Credit Card

The credit card table holds all information on which card is held by which owner/account through the disposition interim table. It also holds information on the card type. The card types are already branded in english so no translational mapping needs to be made.

card = data_loader.load_csv('card', parse_dates={'issued': '%y%m%d'})

card.sample(5)

	card_id	disp_id	type	issued
159	854	6753	classic	1996-10-07
205	137	786	junior	1997-01-10
445	481	3102	junior	1998-01-06
350	19	130	classic	1997-09-09
533	510	3256	classic	1998-04-13

District

The district table contains information on the domicile of the accounts and the domicil of the clients.

This table has numerous variables with id-like names. These need to get renamed for clarity:

district = data_loader.load_csv('district')

district = district.rename(columns={
    'A1': 'district_id',
    'A2': 'district_name',
    'A3': 'region',
    'A4': 'population',
    'A5': 'n_municipalities_with_inhabitants_lt_499',
    'A6': 'n_municipalities_with_inhabitants_500_to_1999',
    'A7': 'n_municipalities_with_inhabitants_2000_to_9999',
    'A8': 'n_municipalities_with_inhabitants_gt_10000',
    'A9': 'n_cities',
    'A10': 'ratio_urban_inhabitants',
    'A11': 'average_salary',
    'A12': 'unemployment_rate_95',
    'A13': 'unemployment_rate_96',
    'A14': 'enterpreneurs_per_1000_inhabitants',
    'A15': 'n_commited_crimes_95',
    'A16': 'n_commited_crimes_96'
})

district.sample(5)

	district_id	district_name	region	population	n_municipalities_with_inhabitants_lt_499	n_municipalities_with_inhabitants_500_to_1999	n_municipalities_with_inhabitants_2000_to_9999	n_municipalities_with_inhabitants_gt_10000	n_cities	ratio_urban_inhabitants	average_salary	unemployment_rate_95	unemployment_rate_96	enterpreneurs_per_1000_inhabitants	n_commited_crimes_95	n_commited_crimes_96
70	71	Novy Jicin	north Moravia	161227	5	35	11	4	10	69.7	8678	5.93	5.57	102	4980	4595
72	73	Opava	north Moravia	182027	17	49	12	2	7	56.4	8746	3.33	3.74	90	4355	4433
75	76	Sumperk	north Moravia	127369	31	32	13	2	7	51.2	8369	4.73	5.88	107	3736	2807
25	26	Plzen - mesto	west Bohemia	170449	0	0	0	1	1	100.0	10787	2.64	3.09	131	6041	6261
55	56	Breclav	south Moravia	124605	11	42	14	1	8	46.0	8772	4.69	4.98	126	2854	3158

Data Merging

Since every table was now loaded into memory, the next goal is to merge the data together into one Client Dataframe (client_df). The centerpiece in this merging process is the account table since it is the heartpiece of the dataset and all relations center around it.

from src.data_utils import add_prefix_except_id

account = add_prefix_except_id(account, 'account_', id_exceptions=['district_id'])
client_df = disp.merge(account, on='account_id', how='left')

client = add_prefix_except_id(client, 'client_', id_exceptions=['district_id'])
client_df = client_df.merge(client, on='client_id', how='left')

order = add_prefix_except_id(order_pivot, 'ordertype_')
client_df = client_df.merge(order, on='account_id', how='left')

loan = add_prefix_except_id(loan, 'loan_')
client_df = client_df.merge(loan, on='account_id', how='left')

card = add_prefix_except_id(card, 'card_')
client_df = client_df.merge(card, on='disp_id', how='left')

client_district = add_prefix_except_id(district, 'client_district_')
client_df = client_df.merge(client_district, left_on='client_district_id', right_on='district_id', how='left')

account_district = add_prefix_except_id(district, 'account_district_')
client_df = client_df.merge(account_district, left_on='account_district_id', right_on='district_id', how='left')

client_df.sample(5)

n_merged_base_client = client_df.shape[0]

To see if we now have one row for each account, the following assertion is being made:

assert client_df['account_id'].nunique() == client_df.shape[0]

The assertion runs through, therefore we can assume that the merging process was successful and no account was accidentally merged more than once into the client_df dataframe.

Data Cleaning

The next step is to clean the dataset.

Removing Junior Cards

The task at hand requires to only predict if a customer will get a classic or gold card. Junior cards are to be dismissed in this task.

junior_clients = client_df[client_df['card_type'] == 'junior']

client_df = client_df[~client_df['account_id'].isin(junior_clients['account_id'])]

transaction = transaction[~transaction['account_id'].isin(junior_clients['account_id'])]

client_df['has_card'] = client_df['card_id'].notnull()

print(f'Number of junior clients: {junior_clients.shape[0]}')
print(f'Number of clients remaining: {client_df.shape[0]}')

Number of junior clients: 145
Number of clients remaining: 4355

Additionally, clients without cards could potentially be in the age range of someone who would naturally get a Junior Card. Since there is no information on how these special cases should be handled, the clients in that age bracket will be removed in the following step.

To find out what clients should get removed from the dataset, firts the age distribution needs to be examined to find a suitable “cutoff age”.

JUNIOR_AGE_CUTOFF = 21

fig, ax = plt.subplots(1, 2, figsize=(15, 5))

sns.histplot(junior_clients['client_age'], bins=40, ax=ax[0], kde=True)
ax[0].axvline(JUNIOR_AGE_CUTOFF, color='r', linestyle='--')
ax[0].text(JUNIOR_AGE_CUTOFF - .25, 10, 'Junior Age Cutoff', rotation=90, color='r')
ax[0].set_title('Junior Clients Age Distribution')
ax[0].set_xlabel('Age')

sns.histplot(client_df['client_age'], bins=20, ax=ax[1], color='b', kde=True)
ax[1].axvline(JUNIOR_AGE_CUTOFF, color='r', linestyle='--')
ax[1].text(JUNIOR_AGE_CUTOFF - 2, 100, 'Junior Age Cutoff', rotation=90, color='r')
ax[1].set_title('Non-Junior Clients Age Distribution')
ax[1].set_xlabel('Age')

plt.show()

For the cutoff the age 21 was chosen (meaning non-card-holders that are younger than the threshold or exactly at the threshold) since we can see a rapid decrease in Junior Card Holders after the proposed age. These remaining customers above the cutoff age that still own a Junior card could just remain there due to the reason that the bank may need some time to reissue regular cards; Which is why no clear cutoff age can be found in the distribution.

underage_clients = client_df[(client_df['client_age'] <= JUNIOR_AGE_CUTOFF) & (~client_df['has_card'])]

client_df = client_df[~client_df['client_id'].isin(underage_clients['client_id'])]
transaction = transaction[~transaction['account_id'].isin(underage_clients['account_id'])]

print(f'Number of underage clients: {underage_clients.shape[0]}')
print(f'Number of clients remaining after underage client removal: {client_df.shape[0]}')

Number of underage clients: 222
Number of clients remaining after underage client removal: 4133

Model Construction

In the model construction step the main goal is to build a “counter quantity”. The PKDD’99 dataset unfortunately only labelled preexisiting customers with an issue date. Customers who decided against a card may not be in the dataset or are at least not marked in such a way.

Processing Transactional Data

The first task is to look if every account in the transactions dataframe has a “first transaction”. This would make the calculation of the monthly balance much easier since everything can be summed up without having to worry that there were months without records in the transaction dataframe.

# Find the minimum (first) transaction(s) date for each account
min_dates = transaction.groupby('account_id')['date'].min().reset_index()
min_dates.rename(columns={'date': 'min_date'}, inplace=True)

# Merge the minimum date back to the transactions to identify all transactions on the first day
transactions_with_min_date = pd.merge(transaction, min_dates, on='account_id')

# Filter transactions that are on the first day
first_day_transactions = transactions_with_min_date[transactions_with_min_date['date'] == transactions_with_min_date['min_date']]
first_day_transactions = first_day_transactions.copy()

# Now, for each of these first day transactions, check if any have amount equals balance
first_day_transactions['amount_equals_balance'] = first_day_transactions['amount'] == first_day_transactions['balance']

# Group by account_id and check if any transactions for each account meet the condition
accounts_meeting_condition = first_day_transactions.groupby('account_id')['amount_equals_balance'].any().reset_index()

# Verify if all accounts have at least one transaction on the first day meeting the condition
all_accounts_covered = accounts_meeting_condition['amount_equals_balance'].all()

print("Does every account's first day of transactions include at least one transaction where amount equals balance?", all_accounts_covered)

Does every account's first day of transactions include at least one transaction where amount equals balance? True

Since the goal is to have a roll-up window per month, the transactions need to get modified so that they aggregate their values on a per-month level.

The code first groups the transactions by account_id and month, and aggregates the data to calculate monthly volume, credit, withdrawal, and the number of transactions (n_transactions). After that the minimum and maximum month for each account_id gets calculated.

Finally, the cumulative sum of the volume column for each account_id is calculated, resulting in a running balance over time.

transaction['month'] = transaction['date'].dt.to_period('M')

transactions_monthly = transaction.groupby(['account_id', 'month']).agg(
    volume=('amount', 'sum'),
    credit=('amount', lambda x: x[x > 0].sum()),
    withdrawal=('amount', lambda x: x[x < 0].sum()),
    n_transactions=('amount', 'size')
).reset_index()

transactions_monthly['month'] = pd.PeriodIndex(transactions_monthly['month'])

date_ranges = transactions_monthly.groupby('account_id')['month'].agg(['min', 'max'])

def reindex_df(group, account_id):
    idx = pd.period_range(start=group['month'].min(), end=group['month'].max(), freq='M')
    group.set_index('month', inplace=True)
    group = group.reindex(idx, fill_value=0)
    group.reset_index(inplace=True)
    group.rename(columns={'index': 'month'}, inplace=True)
    group['account_id'] = account_id
    return group

transactions_monthly = (transactions_monthly.groupby('account_id')
                        .apply(lambda x: reindex_df(x, x.name))
                        .reset_index(level=0, drop=True))

transactions_monthly['balance'] = transactions_monthly.groupby('account_id')['volume'].cumsum()

transactions_monthly.sample(5)

	month	account_id	volume	credit	n_transactions	balance
5	1998-01	2644	17485.7	17485.7	13	46045.3
22	1995-05	1297	42004.4	42004.4	7	933208.2
15	1998-08	250	11099.8	11099.8	6	202891.1
23	1998-08	1258	5210.7	5210.7	6	127985.3
36	1996-05	3316	33962.0	33962.0	5	1210751.5

Defining Roll-Up Windows of Transactions

Before we can continue to construct the roll-up windows for all customers, we need to propose a hypothetical card_issued date for the clients that have no card (negative samples).

To not introduce a new bias towards card-holders or non-card-holders the idea here is to sample issue dates from the card holders. An important point is to eliminate or surpress seasonality as good as possible (hence a 13 month roll-up window will be constructed). Such seasonality could also emerge if we just sample random dates - To not encounter that issue and get a counter set to our existing card holders, the distribution of days between account creation and the issue date is used. This way we can ensure that we don’t sample dates that lie outside of a non-card customer’s account activity.

clients_with_cards = client_df[~client_df['card_issued'].isnull()]
time_between_creation_and_issue = (clients_with_cards['card_issued'] - clients_with_cards['account_date']).dt.days

plt.figure(figsize=(10, 6))
sns.histplot(time_between_creation_and_issue, bins=50, stat='percent', kde=False, color='blue', linewidth=1)

plt.title('Distribution of Days between Account Creation and Card Issue Date')
plt.xlabel('Days')
plt.ylabel('Percentage of Accounts')
plt.grid(True)

plt.show()

When looking at the distribution of how many days it usually takes an account to get a card is that usually cards get issued the earliest 200 days after account creation. The issuances decrease steadily but in rare cases it can even take a customer over 5 years (>2000 days) to decide to get a credit card.

Based on this distribution we now will sample the deltas (time between account creation and issuance) for our non-card holders.

np.random.seed(1337)

sampled_deltas = np.random.choice(time_between_creation_and_issue, size=len(client_df[client_df['card_issued'].isnull()]))

plt.figure(figsize=(10, 6))
sns.histplot(sampled_deltas, bins=50, stat='percent', kde=False, color='blue', linewidth=1, label='Sampled Deltas')
sns.histplot(time_between_creation_and_issue, bins=50, stat='percent', kde=False, color='orange', linewidth=1, label='Original Deltas')

plt.title('Distribution of Days between Account Creation and Card Issue Date')
plt.xlabel('Days')
plt.ylabel('Percentage of Accounts')
plt.legend()
plt.grid(True)

plt.show()

Through the random sampling we now can use every negative sample from the dataset. Additionally, through random sampling we added a small amount of noise which is expected to have a generalizing effect on the data.

To check if we now have an issue date for all customers the following check is executed:

if len(client_df[client_df['card_issued'].isnull()]) > 0:
    client_df.loc[client_df['card_issued'].isnull(), 'card_issued'] = client_df.loc[client_df['card_issued'].isnull(), 'account_date'] + pd.to_timedelta(sampled_deltas, unit='D')
    
print(f'Number of NaT/NaN values in card_issued: {client_df["card_issued"].isnull().sum()}')

Number of NaT/NaN values in card_issued: 0

The check shows that we now successfully sampled an issue date for all customers.

card_issued = card.groupby('disp_id')['card_issued'].min().reset_index()
card_issued.head(5)

	disp_id	card_issued
0	9	1998-10-16
1	19	1998-03-13
2	41	1995-09-03
3	42	1998-11-26
4	51	1995-04-24

We now can merge together the issued dates to the monthly transaction aggregation.

transactions_monthly = transactions_monthly.merge(client_df[['account_id', 'card_issued']], left_on='account_id', right_on='account_id', how='left')

transactions_monthly.head()

	month	account_id	volume	credit	n_transactions	balance	card_issued
0	1995-03	1	1000.0	1000.0	1	1000.0	1998-09-18
1	1995-04	1	16298.2	16298.2	3	17298.2	1998-09-18
2	1995-05	1	5858.0	5858.0	3	23156.2	1998-09-18
3	1995-06	1	3979.6	3979.6	3	27135.8	1998-09-18
4	1995-07	1	9087.9	9087.9	3	36223.7	1998-09-18

Now, let’s see if the join worked correctly and we don’t have any transactions without a card_issued date anymore.

assert transactions_monthly['card_issued'].isnull().sum() == 0

Validating Negative Examples

The goal for our model will be to be able to learn from a base set of customers (with and without cards) that lived through the same economic circumstances. To validate that all customers in our current set are represented in a well distributed way, this next plot looks at the distribution of volume throughout the datasets timeframe.

transactions_monthly['has_card'] = transactions_monthly['account_id'].isin(client_df[client_df['has_card']]['account_id'])

transactions_monthly.reset_index(inplace=True)
transactions_monthly['id'] = range(len(transactions_monthly))
transactions_monthly.set_index('id', inplace=True)

transactions_with_cards = transactions_monthly[transactions_monthly['has_card'] == True]
transactions_without_cards = transactions_monthly[transactions_monthly['has_card'] == False]

grouped_with_cards = transactions_with_cards.groupby('month')['volume'].sum().reset_index()
grouped_without_cards = transactions_without_cards.groupby('month')['volume'].sum().reset_index()

grouped_with_cards['month'] = grouped_with_cards['month'].astype(str)
grouped_without_cards['month'] = grouped_without_cards['month'].astype(str)

grouped_with_cards['avg_volume_per_client'] = grouped_with_cards['volume'] / transactions_with_cards.shape[0]
grouped_without_cards['avg_volume_per_client'] = grouped_without_cards['volume'] / transactions_without_cards.shape[0]

plt.figure(figsize=(12, 6))

sns.lineplot(x='month', y='avg_volume_per_client', data=grouped_with_cards, marker='o', label='Clients with Cards')
sns.lineplot(x='month', y='avg_volume_per_client', data=grouped_without_cards, marker='o', label='Clients without Cards')

plt.title('Transaction Volume per Month')
plt.xlabel('Month')
plt.ylabel('Average Transaction Volume per Client ($)')
plt.legend()
plt.grid(True)

plt.xticks(ticks=grouped_with_cards['month'][grouped_with_cards['month'].str.endswith('-01')], rotation=45)
plt.gca().set_xticklabels([label[:4] for label in grouped_with_cards['month'][grouped_with_cards['month'].str.endswith('-01')]])

plt.show()

We can see that we generally have less transaction volume for clients without cards, though the trends stay the same for both customer types: Around Christmas and New Year’s Eve the transaction volume rises rapidly. The same observation can be seen in the middle of each year, perhaps where customers go on summer vacation.

In order to construct the Roll-Up Window for every customer, the difference of each month to the issue date’s month needs to get calculated. After calculating the difference at each month the size of the Roll-Up window can be determined. In this case a window of 13 months was chosen, so a year plus a lag month which aims to capture the client’s decision time or the banks time until the card has been sent out to the customer.

transactions_monthly['card_issued'] = pd.to_datetime(transactions_monthly['card_issued'])
transactions_monthly['card_issued_period'] = transactions_monthly['card_issued'].dt.to_period('M')

transactions_monthly['month_diff'] = transactions_monthly.apply(lambda row: (row['card_issued_period'] - row['month']).n if pd.notnull(row['card_issued_period']) and pd.notnull(row['month']) else None, axis=1)

filtered_transactions = transactions_monthly[transactions_monthly['month_diff'].between(1, 13)]

transactions_monthly.sample(5)

	index	month	account_id	volume	credit	withdrawal	n_transactions	balance	card_issued	has_card	card_issued_period	month_diff
id
44634	44634	1998-05	1252	47796.5	47796.5	0.0	5	1777803.9	1999-05-20	False	1999-05	12
44011	44011	1997-07	1235	10518.0	10518.0	0.0	6	373341.3	1995-12-18	False	1995-12	-19
95818	95818	1997-12	2651	98919.1	98919.1	0.0	6	4442381.9	1995-07-09	True	1995-07	-29
73459	73459	1996-08	2035	52500.5	52500.5	0.0	3	155070.1	2000-06-14	False	2000-06	46
162431	162431	1995-08	8998	1900.0	1900.0	0.0	1	21400.0	1997-11-23	False	1997-11	27

The sample and assertions shows, as expected, the dataframe now has a month_diff column describing how many months until the issue month.

Pivoting the transactions

An issue at this point can be that an account may have months without any transactions, so there needs to be a more thorough process to interpolate the data: - Volume, Withdrawal, Credit and Number of Transactions: If there are missing months in these variables we can just set 0 as their value as there has not been any activity if there were no recorded months. - Balance: The balance will get recursively set to the last preceding recorded month. So if there are consecutive “missing” months in transactions the balance will always be set to the last recorded month.

account_summary = pd.DataFrame(filtered_transactions['account_id'].unique(), columns=['account_id'])

variables_to_pivot = ['volume', 'withdrawal', 'credit', 'n_transactions', 'balance']

for variable in variables_to_pivot:
    grouped = filtered_transactions.groupby(['account_id', 'month_diff'])[variable].sum().reset_index()
    pivot = grouped.pivot(index='account_id', columns='month_diff', values=variable).reset_index()
    pivot.columns = ['account_id'] + [f'{variable}_month_diff_{int(col)}' if col != 'account_id' else 'account_id' for col in pivot.columns[1:]]
    account_summary = pd.merge(account_summary, pivot, on='account_id', how='left')

for variable in ['volume', 'withdrawal', 'credit', 'n_transactions']:
    account_summary.update(account_summary.filter(regex=f'^{variable}_').fillna(0))

def find_last_balance(account_id, starting_month_diff):
    higher_month_diff = transactions_monthly[
        (transactions_monthly['account_id'] == account_id) & 
        (transactions_monthly['month_diff'] >= starting_month_diff)
    ].sort_values('month_diff')
    
    last_balance_row = higher_month_diff[higher_month_diff['balance'].notna()].head(1)
    
    if not last_balance_row.empty:
        return last_balance_row.iloc[0]['balance']
    else:
        return 0

balance_columns = [col for col in account_summary.columns if 'balance_month_diff_' in col]
for idx, row in account_summary.iterrows():
    for col in balance_columns:
        if pd.isna(row[col]):
            month_diff = int(col.split('_')[-1])
            last_balance = find_last_balance(row['account_id'], month_diff + 1)
            account_summary.at[idx, col] = last_balance
            
account_summary.sample(5)

As we can see, the columns get pivoted correctly and now every account has its 13-month roll-up window so we can merge together the window with the existing client dataframe (main dataframe).

client_df = client_df.merge(account_summary, on='account_id', how='inner')

Bringing the data together

Since we now are done with the main model construction, the following visualization aims to show and recap the process we went through with the data at hand.

n_lt_13_month_hist = transactions_monthly['account_id'].nunique()-filtered_transactions['account_id'].nunique()

preprocessing_summary = [
        n_merged_base_client,
        -len(junior_clients),
        -n_lt_13_month_hist,
        -len(underage_clients),
        len(client_df)
]

fig = go.Figure(go.Waterfall(
        name = "20", orientation = "v",
        measure = ["absolute", "relative", "relative", "relative", "total"],
        x = ["Base Client List", 
             "Junior Clients", 
             "Clients with less than 13 Months of Transaction History", 
             "Non-Card-Holders that are underage", 
             "Remaining Clients"],
        textposition = "outside",
        y = preprocessing_summary,
        text = [str(x) for x in preprocessing_summary],
        connector = {"line":{"color":"rgb(63, 63, 63)"}},
))

fig.update_layout(
        title = "Profit and loss statement 2018",
        showlegend = True
)

fig.show()

The baseline dataset consists of 4500 clients. Of these 4500 clients 145 are Junior Clients and those need to get removed since the task at hand does not focus on this customer group. After the Junior card removal clients with less than 13 months of history were discarded since those do not have enough time as a customer to build a roll-up window and therefore would not be suitable to make any predictions with. An implicit dependency with the Junior cards is the age - Customers without cards that may fall into the category of Junior customers also got discarded since our model would not be suitable to classify if such “underage” customers should get a card or not. The remaining dataset or “golden record” therefore consists of 3715 customers.

Exploratory Data Analysis

Exploring Missing Values

missing_values = client_df.isnull().sum()

missing_values = pd.DataFrame({
    'Number of Missing Values': missing_values[missing_values > 0],
    'Percentage of Missing Values': [
        f"{x:.2f}%" for x in (missing_values[missing_values > 0] / client_df.shape[0] * 100).round(2)
    ]
}, columns=['Number of Missing Values', 'Percentage of Missing Values'])

fig = go.Figure()

fig.add_trace(go.Bar(
    x=missing_values.index,
    y=missing_values['Number of Missing Values'],
    text=missing_values['Percentage of Missing Values'],
    textposition='auto'
))

fig.update_layout(
    title='Missing Values in the Dataset',
    xaxis_title='Columns',
    yaxis_title='Number of Missing Values'
)

fig.show()

Cardholders vs Non-Cardholders

card_owners = client_df[client_df['has_card']]
non_card_owners = client_df[~client_df['has_card']]

data = {
    'Card Ownership': ['Card Owners', 'Non-Card Owners'],
    'Number of Clients': [card_owners.shape[0], non_card_owners.shape[0]]
}

plt.figure(figsize=(10, 6))
sns.barplot(x='Card Ownership', y='Number of Clients', data=data, palette='viridis', hue='Card Ownership')

for index, value in enumerate(data['Number of Clients']):
    plt.text(index, value, str(value), ha='center', va='bottom')

plt.title('Card Owners vs Non-Card Owners')
plt.xlabel('Card Ownership')
plt.ylabel('Number of Clients')
plt.grid(True)

plt.show()

Card Types

card_types = card_owners['card_type'].value_counts()
card_types = card_types.reset_index()
card_types.columns = ['Card Type', 'Number of Clients']

plt.figure(figsize=(10, 6))
sns.barplot(x='Card Type', y='Number of Clients', data=card_types, palette='viridis', hue='Card Type')

for index, value in enumerate(card_types['Number of Clients']):
    plt.text(index, value, str(value), ha='center', va='bottom')

plt.title('Card Types of Card Owners')
plt.xlabel('Card Type')
plt.ylabel('Number of Clients')
plt.grid(True)

plt.show()

Distribution of Issuance Dates

plt.figure(figsize=(10, 6))
sns.histplot(card_owners['card_issued'], bins=50, kde=True, color='blue', label='Card Owners')
sns.histplot(non_card_owners['card_issued'], bins=50, kde=True, color='orange', label='Non-Card Owners')

plt.title('Distribution of Card Issue Date')
plt.xlabel('Issue Date')
plt.ylabel('Number of Clients')
plt.legend()
plt.grid(True)

plt.show()

Balance and Volume over time

account_14 = transactions_monthly[transactions_monthly['account_id'] == 14].copy()
account_14['month'] = account_14['month'].dt.to_timestamp()

account_18 = transactions_monthly[transactions_monthly['account_id'] == 18].copy()
account_18['month'] = account_18['month'].dt.to_timestamp()

plt.figure(figsize=(10, 6))
sns.lineplot(data=account_14, x='month', y='balance', marker='o', label='Account 14')
sns.lineplot(data=account_18, x='month', y='balance', marker='o', label='Account 18')

plt.axvspan(account_14['card_issued'].min() - pd.DateOffset(months=13), account_14['card_issued'].min(), color='r', alpha=0.3)
plt.axvspan(account_18['card_issued'].min() - pd.DateOffset(months=13), account_18['card_issued'].min(), color='b', alpha=0.3)

plt.text(account_14['card_issued'].min() - pd.DateOffset(months=13), 500000, '13 Month Rollup Window of Account 14', rotation=90, color='r')
plt.text(account_18['card_issued'].min() - pd.DateOffset(months=13), 500000, '13 Month Rollup Window of Account 18', rotation=90, color='b')

plt.title('Balance Over Time for Account 14 and Account 18')
plt.xlabel('Month')
plt.ylabel('Balance')
plt.legend()
plt.grid(True)

plt.show()

plt.figure(figsize=(10, 6))
sns.lineplot(x='month', y='volume', data=account_14, marker='o', label='Account 14', color='r')
sns.lineplot(x='month', y='volume', data=account_18, marker='o', label='Account 18', color='b')

plt.axvspan(account_14['card_issued'].min() - pd.DateOffset(months=13), account_14['card_issued'].min(), color='r', alpha=0.3)
plt.axvspan(account_18['card_issued'].min() - pd.DateOffset(months=13), account_18['card_issued'].min(), color='b', alpha=0.3)

plt.text(account_14['card_issued'].min() - pd.DateOffset(months=13), 5000, '13 Month Rollup Window of Account 14', rotation=90, color='r')
plt.text(account_18['card_issued'].min() - pd.DateOffset(months=13), 5000, '13 Month Rollup Window of Account 18', rotation=90, color='b')

plt.title('Volume Over Time for Account 14 and Account 18')
plt.xlabel('Month')
plt.ylabel('Volume')
plt.legend()
plt.grid(True)

Volume before card purchase

classic_card_type_df = card_owners[card_owners['card_type'] == 'classic']
gold_card_type_df = card_owners[card_owners['card_type'] == 'gold']

volume_month_diff_columns = [col for col in account_summary.columns if 'volume_month_diff_' in col]

classic_data = []
gold_data = []

for col in volume_month_diff_columns:
    month_num = col.split('_')[-1]
    for value in classic_card_type_df[col]:
        classic_data.append({'Month': month_num, 'Volume': value, 'Card Type': 'Classic'})

for col in volume_month_diff_columns:
    month_num = col.split('_')[-1]
    for value in gold_card_type_df[col]:
        gold_data.append({'Month': month_num, 'Volume': value, 'Card Type': 'Gold'})

combined_data = classic_data + gold_data

combined_df = pd.DataFrame(combined_data)

fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 14), sharex=True)

sns.boxplot(ax=axes[0], x='Month', y='Volume', data=combined_df[combined_df['Card Type'] == 'Classic'], palette='viridis', hue='Month')
axes[0].set_title('Volume of Transactions for Classic Card Holders')
axes[0].set_xlabel('Months before purchase')
axes[0].set_ylabel('Volume of Transactions')

sns.boxplot(ax=axes[1], x='Month', y='Volume', data=combined_df[combined_df['Card Type'] == 'Gold'], palette='viridis', hue='Month')
axes[1].set_title('Volume of Transactions for Gold Card Holders')
axes[1].set_xlabel('Months before purchase')
axes[1].set_ylabel('Volume of Transactions')

fig.suptitle('Volume of Transactions by Card Type')
plt.tight_layout(rect=[0, 0, 1, 0.97])

plt.show()

Age distribution

fig, ax1 = plt.subplots(figsize=(12, 6))

# Plot histograms with percentage normalization on the left y-axis
sns.histplot(card_owners['client_age'], bins=20, stat='percent', kde=False, color='blue', edgecolor='white', linewidth=1, label='Card Owners - Histogram', alpha=0.5, ax=ax1)
sns.histplot(non_card_owners['client_age'], bins=20, stat='percent', kde=False, color='orange', edgecolor='white', linewidth=1, label='Non-Card Owners - Histogram', alpha=0.5, ax=ax1)

# Set up the second y-axis
ax2 = ax1.twinx()

# Plot density plots on the right y-axis
sns.kdeplot(card_owners['client_age'], bw_adjust=0.5, color='blue', label='Card Owners - Density', ax=ax2)
sns.kdeplot(non_card_owners['client_age'], bw_adjust=0.5, color='orange', label='Non-Card Owners - Density', ax=ax2)

# Set the titles and labels
ax1.set_title('Distribution of Client Age')
ax1.set_xlabel('Age')
ax1.set_ylabel('Percentage of Clients')
ax2.set_ylabel('Density')

# Combine legends
handles1, labels1 = ax1.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(handles1 + handles2, labels1 + labels2, loc='upper right')

# Show the plot
plt.grid(True)
plt.show()

Feature Engineering

Demographic-Based Features

Defining Age Groups The pd.cut() function bins continuous data into discrete intervals based on specified edges, with the default behavior including the left edge and excluding the right edge of each interval. The ages get binned by assigning each age value to an interval defined by specified edges, such as [0, 20), [20, 30), and so on, where each interval includes the left edge and excludes the right edge by default.

age_brackets = [0, 20, 30, 40, 50, 60, 70, 100]

plt.figure(figsize=(12, 6))
sns.histplot(card_owners['client_age'], bins=age_brackets, stat='percent', kde=False, color='blue', edgecolor='white', linewidth=1, label='Card Owners', alpha=0.5)
sns.histplot(non_card_owners['client_age'], bins=age_brackets, stat='percent', kde=False, color='orange', edgecolor='white', linewidth=1, label='Non-Card Owners', alpha=0.5)

for age_bracket in age_brackets:
    plt.axvline(age_bracket, color='black', linestyle='--', linewidth=1)
    
plt.title('Age Distribution of Card Owners and Non-Card Owners')
plt.xlabel('Age')
plt.ylabel('Percentage of Clients')

for i in range(len(age_brackets) - 1):
    plt.text(age_brackets[i] + 3, 5, f'{age_brackets[i]}-{age_brackets[i+1]-1}', fontsize=8, color='black')
    
plt.legend()
plt.grid(True)

plt.show()

client_df['age_bracket'] = pd.cut(client_df['client_age'], bins=age_brackets, right=False)

age_bracket_counts = client_df['age_bracket'].value_counts().reset_index()
age_bracket_counts.columns = ['Age Bracket', 'Number of Clients']

age_bracket_counts

	Age Bracket	Number of Clients
0	[50, 60)	767
1	[40, 50)	732
2	[30, 40)	729
3	[20, 30)	611
4	[60, 70)	460
5	[70, 100)	416
6	[0, 20)	0

Age at Loan

client_df['client_age_at_loan'] = (pd.to_datetime(client_df['loan_date']) - pd.to_datetime(client_df['client_birth_date'])).dt.days // 365

client_df.head(5)

	disp_id	client_id	account_id	account_district_id	account_frequency	account_date	client_district_id	client_gender	client_birth_date	client_age	ordertype_HOUSEHOLD	ordertype_LOAN PAYMENT	loan_id	loan_date	loan_amount	loan_duration	loan_payments	loan_status	card_id	card_type	card_issued	district_id_x	client_district_district_name	client_district_region	client_district_population	client_district_n_municipalities_with_inhabitants_lt_499	client_district_n_municipalities_with_inhabitants_500_to_1999	client_district_n_municipalities_with_inhabitants_2000_to_9999	client_district_n_municipalities_with_inhabitants_gt_10000	client_district_n_cities	client_district_ratio_urban_inhabitants	client_district_average_salary	client_district_unemployment_rate_95	client_district_unemployment_rate_96	client_district_enterpreneurs_per_1000_inhabitants	client_district_n_commited_crimes_95	client_district_n_commited_crimes_96	district_id_y	account_district_district_name	account_district_region	account_district_population	account_district_n_municipalities_with_inhabitants_lt_499	account_district_n_municipalities_with_inhabitants_500_to_1999	account_district_n_municipalities_with_inhabitants_2000_to_9999	account_district_n_municipalities_with_inhabitants_gt_10000	account_district_n_cities	account_district_ratio_urban_inhabitants	account_district_average_salary	account_district_unemployment_rate_95	account_district_unemployment_rate_96	account_district_enterpreneurs_per_1000_inhabitants	account_district_n_commited_crimes_95	account_district_n_commited_crimes_96	has_card	volume_month_diff_1	volume_month_diff_2	volume_month_diff_3	volume_month_diff_4	volume_month_diff_5	volume_month_diff_6	volume_month_diff_7	volume_month_diff_8	volume_month_diff_9	volume_month_diff_10	volume_month_diff_11	volume_month_diff_12	volume_month_diff_13	credit_month_diff_1	credit_month_diff_2	credit_month_diff_3	credit_month_diff_4	credit_month_diff_5	credit_month_diff_6	credit_month_diff_7	credit_month_diff_8	credit_month_diff_9	credit_month_diff_10	credit_month_diff_11	credit_month_diff_12	credit_month_diff_13	n_transactions_month_diff_1	n_transactions_month_diff_2	n_transactions_month_diff_3	n_transactions_month_diff_4	n_transactions_month_diff_5	n_transactions_month_diff_6	n_transactions_month_diff_7	n_transactions_month_diff_8	n_transactions_month_diff_9	n_transactions_month_diff_10	n_transactions_month_diff_11	n_transactions_month_diff_12	n_transactions_month_diff_13	balance_month_diff_1	balance_month_diff_2	balance_month_diff_3	balance_month_diff_4	balance_month_diff_5	balance_month_diff_6	balance_month_diff_7	balance_month_diff_8	balance_month_diff_9	balance_month_diff_10	balance_month_diff_11	balance_month_diff_12	balance_month_diff_13	age_bracket	client_age_at_loan
0	1	1	1	18	MONTHLY CHARGES	1995-03-24	18	FEMALE	1970-12-13	29	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-09-18	18	Pisek	south Bohemia	70699	60	13	2	1	4	65.3	8968	2.83	3.35	131	1740	1910	18	Pisek	south Bohemia	70699	60	13	2	1	4	65.3	8968	2.83	3.35	131	1740	1910	False	6492.7	6221.0	6667.1	6212.3	7435.5	7018.6	6701.9	9091.5	10907.2	7318.0	6218.0	6600.6	8282.7	6492.7	6221.0	6667.1	6212.3	7435.5	7018.6	6701.9	9091.5	10907.2	7318.0	6218.0	6600.6	8282.7	5.0	4.0	5.0	4.0	6.0	5.0	5.0	10.0	5.0	5.0	4.0	5.0	6.0	338855.2	332362.5	326141.5	319474.4	313262.1	305826.6	298808.0	292106.1	283014.6	272107.4	264789.4	258571.4	251970.8	[20, 30)	NaN
1	2	2	2	1	MONTHLY CHARGES	1993-02-26	1	MALE	1945-02-04	54	1.0	1.0	4959.0	1994-01-05	80952.0	24.0	3373.0	contract finished, no problems	NaN	NaN	1993-11-29	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677	99107	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677	99107	False	34617.6	45943.4	52856.1	47098.6	52913.9	37980.7	31345.5	23949.5	1100.0	0.0	0.0	0.0	0.0	34617.6	45943.4	52856.1	47098.6	52913.9	37980.7	31345.5	23949.5	1100.0	0.0	0.0	0.0	0.0	6.0	7.0	7.0	6.0	3.0	3.0	3.0	3.0	1.0	0.0	0.0	0.0	0.0	327805.3	293187.7	247244.3	194388.2	147289.6	94375.7	56395.0	25049.5	1100.0	0.0	0.0	0.0	0.0	[50, 60)	48.0
2	6	6	4	12	MONTHLY CHARGES	1996-02-21	12	MALE	1919-09-22	80	2.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1997-10-26	12	Pribram	central Bohemia	107870	84	29	6	1	6	58.0	8754	3.83	4.31	137	3804	3868	12	Pribram	central Bohemia	107870	84	29	6	1	6	58.0	8754	3.83	4.31	137	3804	3868	False	10917.6	12013.8	9011.7	14254.5	11343.0	9015.0	10769.2	9003.4	20249.4	13529.7	9040.6	9031.2	12329.5	10917.6	12013.8	9011.7	14254.5	11343.0	9015.0	10769.2	9003.4	20249.4	13529.7	9040.6	9031.2	12329.5	6.0	6.0	5.0	6.0	7.0	5.0	6.0	5.0	12.0	7.0	5.0	5.0	6.0	195475.7	184558.1	172544.3	163532.6	149278.1	137935.1	128920.1	118150.9	109147.5	88898.1	75368.4	66327.8	57296.6	[70, 100)	NaN
3	7	7	5	15	MONTHLY CHARGES	1997-05-30	15	MALE	1929-01-25	70	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-04-27	15	Cesky Krumlov	south Bohemia	58796	22	16	7	1	5	51.9	9045	3.13	3.60	124	1845	1879	15	Cesky Krumlov	south Bohemia	58796	22	16	7	1	5	51.9	9045	3.13	3.60	124	1845	1879	False	8607.1	7797.2	15720.3	10825.1	7812.5	5035.1	5017.0	5017.0	5017.0	5017.0	600.0	0.0	0.0	8607.1	7797.2	15720.3	10825.1	7812.5	5035.1	5017.0	5017.0	5017.0	5017.0	600.0	0.0	0.0	5.0	4.0	10.0	6.0	4.0	3.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	76465.3	67858.2	60061.0	44340.7	33515.6	25703.1	20668.0	15651.0	10634.0	5617.0	600.0	0.0	0.0	[70, 100)	NaN
4	8	8	6	51	MONTHLY CHARGES	1994-09-27	51	FEMALE	1938-02-21	61	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-11-27	51	Trutnov	east Bohemia	121947	37	28	7	3	11	70.5	8541	2.51	2.97	131	3496	3839	51	Trutnov	east Bohemia	121947	37	28	7	3	11	70.5	8541	2.51	2.97	131	3496	3839	False	14333.0	10810.0	10798.3	11989.9	20888.8	13055.1	10807.0	13039.2	10792.4	23857.9	11865.2	10815.6	10803.9	14333.0	10810.0	10798.3	11989.9	20888.8	13055.1	10807.0	13039.2	10792.4	23857.9	11865.2	10815.6	10803.9	5.0	4.0	4.0	5.0	6.0	5.0	4.0	5.0	4.0	11.0	6.0	4.0	4.0	625481.9	611148.9	600338.9	589540.6	577550.7	556661.9	543606.8	532799.8	519760.6	508968.2	485110.3	473245.1	462429.5	[60, 70)	NaN

Temporal Features

Account age

client_df['account_age'] = (pd.to_datetime('2000-01-01') - pd.to_datetime(client_df['account_date'])).dt.days // 365

client_df.head(5)

	disp_id	client_id	account_id	account_district_id	account_frequency	account_date	client_district_id	client_gender	client_birth_date	client_age	ordertype_HOUSEHOLD	ordertype_LOAN PAYMENT	loan_id	loan_date	loan_amount	loan_duration	loan_payments	loan_status	card_id	card_type	card_issued	district_id_x	client_district_district_name	client_district_region	client_district_population	client_district_n_municipalities_with_inhabitants_lt_499	client_district_n_municipalities_with_inhabitants_500_to_1999	client_district_n_municipalities_with_inhabitants_2000_to_9999	client_district_n_municipalities_with_inhabitants_gt_10000	client_district_n_cities	client_district_ratio_urban_inhabitants	client_district_average_salary	client_district_unemployment_rate_95	client_district_unemployment_rate_96	client_district_enterpreneurs_per_1000_inhabitants	client_district_n_commited_crimes_95	client_district_n_commited_crimes_96	district_id_y	account_district_district_name	account_district_region	account_district_population	account_district_n_municipalities_with_inhabitants_lt_499	account_district_n_municipalities_with_inhabitants_500_to_1999	account_district_n_municipalities_with_inhabitants_2000_to_9999	account_district_n_municipalities_with_inhabitants_gt_10000	account_district_n_cities	account_district_ratio_urban_inhabitants	account_district_average_salary	account_district_unemployment_rate_95	account_district_unemployment_rate_96	account_district_enterpreneurs_per_1000_inhabitants	account_district_n_commited_crimes_95	account_district_n_commited_crimes_96	has_card	volume_month_diff_1	volume_month_diff_2	volume_month_diff_3	volume_month_diff_4	volume_month_diff_5	volume_month_diff_6	volume_month_diff_7	volume_month_diff_8	volume_month_diff_9	volume_month_diff_10	volume_month_diff_11	volume_month_diff_12	volume_month_diff_13	credit_month_diff_1	credit_month_diff_2	credit_month_diff_3	credit_month_diff_4	credit_month_diff_5	credit_month_diff_6	credit_month_diff_7	credit_month_diff_8	credit_month_diff_9	credit_month_diff_10	credit_month_diff_11	credit_month_diff_12	credit_month_diff_13	n_transactions_month_diff_1	n_transactions_month_diff_2	n_transactions_month_diff_3	n_transactions_month_diff_4	n_transactions_month_diff_5	n_transactions_month_diff_6	n_transactions_month_diff_7	n_transactions_month_diff_8	n_transactions_month_diff_9	n_transactions_month_diff_10	n_transactions_month_diff_11	n_transactions_month_diff_12	n_transactions_month_diff_13	balance_month_diff_1	balance_month_diff_2	balance_month_diff_3	balance_month_diff_4	balance_month_diff_5	balance_month_diff_6	balance_month_diff_7	balance_month_diff_8	balance_month_diff_9	balance_month_diff_10	balance_month_diff_11	balance_month_diff_12	balance_month_diff_13	age_bracket	client_age_at_loan	account_age
0	1	1	1	18	MONTHLY CHARGES	1995-03-24	18	FEMALE	1970-12-13	29	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-09-18	18	Pisek	south Bohemia	70699	60	13	2	1	4	65.3	8968	2.83	3.35	131	1740	1910	18	Pisek	south Bohemia	70699	60	13	2	1	4	65.3	8968	2.83	3.35	131	1740	1910	False	6492.7	6221.0	6667.1	6212.3	7435.5	7018.6	6701.9	9091.5	10907.2	7318.0	6218.0	6600.6	8282.7	6492.7	6221.0	6667.1	6212.3	7435.5	7018.6	6701.9	9091.5	10907.2	7318.0	6218.0	6600.6	8282.7	5.0	4.0	5.0	4.0	6.0	5.0	5.0	10.0	5.0	5.0	4.0	5.0	6.0	338855.2	332362.5	326141.5	319474.4	313262.1	305826.6	298808.0	292106.1	283014.6	272107.4	264789.4	258571.4	251970.8	[20, 30)	NaN	4
1	2	2	2	1	MONTHLY CHARGES	1993-02-26	1	MALE	1945-02-04	54	1.0	1.0	4959.0	1994-01-05	80952.0	24.0	3373.0	contract finished, no problems	NaN	NaN	1993-11-29	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677	99107	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677	99107	False	34617.6	45943.4	52856.1	47098.6	52913.9	37980.7	31345.5	23949.5	1100.0	0.0	0.0	0.0	0.0	34617.6	45943.4	52856.1	47098.6	52913.9	37980.7	31345.5	23949.5	1100.0	0.0	0.0	0.0	0.0	6.0	7.0	7.0	6.0	3.0	3.0	3.0	3.0	1.0	0.0	0.0	0.0	0.0	327805.3	293187.7	247244.3	194388.2	147289.6	94375.7	56395.0	25049.5	1100.0	0.0	0.0	0.0	0.0	[50, 60)	48.0	6
2	6	6	4	12	MONTHLY CHARGES	1996-02-21	12	MALE	1919-09-22	80	2.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1997-10-26	12	Pribram	central Bohemia	107870	84	29	6	1	6	58.0	8754	3.83	4.31	137	3804	3868	12	Pribram	central Bohemia	107870	84	29	6	1	6	58.0	8754	3.83	4.31	137	3804	3868	False	10917.6	12013.8	9011.7	14254.5	11343.0	9015.0	10769.2	9003.4	20249.4	13529.7	9040.6	9031.2	12329.5	10917.6	12013.8	9011.7	14254.5	11343.0	9015.0	10769.2	9003.4	20249.4	13529.7	9040.6	9031.2	12329.5	6.0	6.0	5.0	6.0	7.0	5.0	6.0	5.0	12.0	7.0	5.0	5.0	6.0	195475.7	184558.1	172544.3	163532.6	149278.1	137935.1	128920.1	118150.9	109147.5	88898.1	75368.4	66327.8	57296.6	[70, 100)	NaN	3
3	7	7	5	15	MONTHLY CHARGES	1997-05-30	15	MALE	1929-01-25	70	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-04-27	15	Cesky Krumlov	south Bohemia	58796	22	16	7	1	5	51.9	9045	3.13	3.60	124	1845	1879	15	Cesky Krumlov	south Bohemia	58796	22	16	7	1	5	51.9	9045	3.13	3.60	124	1845	1879	False	8607.1	7797.2	15720.3	10825.1	7812.5	5035.1	5017.0	5017.0	5017.0	5017.0	600.0	0.0	0.0	8607.1	7797.2	15720.3	10825.1	7812.5	5035.1	5017.0	5017.0	5017.0	5017.0	600.0	0.0	0.0	5.0	4.0	10.0	6.0	4.0	3.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	76465.3	67858.2	60061.0	44340.7	33515.6	25703.1	20668.0	15651.0	10634.0	5617.0	600.0	0.0	0.0	[70, 100)	NaN	2
4	8	8	6	51	MONTHLY CHARGES	1994-09-27	51	FEMALE	1938-02-21	61	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-11-27	51	Trutnov	east Bohemia	121947	37	28	7	3	11	70.5	8541	2.51	2.97	131	3496	3839	51	Trutnov	east Bohemia	121947	37	28	7	3	11	70.5	8541	2.51	2.97	131	3496	3839	False	14333.0	10810.0	10798.3	11989.9	20888.8	13055.1	10807.0	13039.2	10792.4	23857.9	11865.2	10815.6	10803.9	14333.0	10810.0	10798.3	11989.9	20888.8	13055.1	10807.0	13039.2	10792.4	23857.9	11865.2	10815.6	10803.9	5.0	4.0	4.0	5.0	6.0	5.0	4.0	5.0	4.0	11.0	6.0	4.0	4.0	625481.9	611148.9	600338.9	589540.6	577550.7	556661.9	543606.8	532799.8	519760.6	508968.2	485110.3	473245.1	462429.5	[60, 70)	NaN	5

Financial Features

Amount of Loan Paid already

client_df['total_loan_amount_paid'] = client_df['loan_payments'] * client_df['loan_duration']

Average Monthly Payment Ratio: Calculate the ratio of monthly loan payment to average salary in the account’s district.

client_df['monthly_payment_ratio'] = client_df['loan_payments'] / client_df['account_district_average_salary']

District Features

Client-Account Region Match: Create a variable indicating if the client and account regions match.

client_df['region_match'] = client_df['client_district_region'] == client_df['account_district_region']

District Population Density: Calculate the population density for client and account districts (if area data is available).

client_df['client_district_population_density'] = client_df['client_district_population'] / client_df['client_district_n_cities']
client_df['account_district_population_density'] = client_df['account_district_population'] / client_df['account_district_n_cities']

Event-Based Statistics

transactions_monthly['card_issued'] = pd.to_datetime(transactions_monthly['card_issued'])

transactions_monthly['month'] = transactions_monthly['month'].apply(lambda x: x.to_timestamp() if isinstance(x, pd.Period) else x)

transactions_filtered = transactions_monthly[transactions_monthly['month'] < transactions_monthly['card_issued']]

stat_columns = ['volume', 'credit', 'withdrawal', 'n_transactions', 'balance']

stats = transactions_filtered.groupby('account_id')[stat_columns].agg(['mean', 'std', 'min', 'max', 'sum', 'median']).reset_index()

stats.columns = ['account_id'] + [f"pre_card_{col[0]}_{col[1]}" for col in stats.columns[1:]]

client_df = client_df.merge(stats, on='account_id', how='left')

client_df.head(5)

	disp_id	client_id	account_id	account_district_id	account_frequency	account_date	client_district_id	client_gender	client_birth_date	client_age	ordertype_HOUSEHOLD	ordertype_LOAN PAYMENT	loan_id	loan_date	loan_amount	loan_duration	loan_payments	loan_status	card_id	card_type	card_issued	district_id_x	client_district_district_name	client_district_region	client_district_population	client_district_n_municipalities_with_inhabitants_lt_499	client_district_n_municipalities_with_inhabitants_500_to_1999	client_district_n_municipalities_with_inhabitants_2000_to_9999	client_district_n_municipalities_with_inhabitants_gt_10000	client_district_n_cities	client_district_ratio_urban_inhabitants	client_district_average_salary	client_district_unemployment_rate_95	client_district_unemployment_rate_96	client_district_enterpreneurs_per_1000_inhabitants	client_district_n_commited_crimes_95	client_district_n_commited_crimes_96	district_id_y	account_district_district_name	account_district_region	account_district_population	account_district_n_municipalities_with_inhabitants_lt_499	account_district_n_municipalities_with_inhabitants_500_to_1999	account_district_n_municipalities_with_inhabitants_2000_to_9999	account_district_n_municipalities_with_inhabitants_gt_10000	account_district_n_cities	account_district_ratio_urban_inhabitants	account_district_average_salary	account_district_unemployment_rate_95	account_district_unemployment_rate_96	account_district_enterpreneurs_per_1000_inhabitants	account_district_n_commited_crimes_95	account_district_n_commited_crimes_96	has_card	volume_month_diff_1	volume_month_diff_2	volume_month_diff_3	volume_month_diff_4	volume_month_diff_5	volume_month_diff_6	volume_month_diff_7	volume_month_diff_8	volume_month_diff_9	volume_month_diff_10	volume_month_diff_11	volume_month_diff_12	volume_month_diff_13	credit_month_diff_1	credit_month_diff_2	credit_month_diff_3	credit_month_diff_4	credit_month_diff_5	credit_month_diff_6	credit_month_diff_7	credit_month_diff_8	credit_month_diff_9	credit_month_diff_10	credit_month_diff_11	credit_month_diff_12	credit_month_diff_13	n_transactions_month_diff_1	n_transactions_month_diff_2	n_transactions_month_diff_3	n_transactions_month_diff_4	n_transactions_month_diff_5	n_transactions_month_diff_6	n_transactions_month_diff_7	n_transactions_month_diff_8	n_transactions_month_diff_9	n_transactions_month_diff_10	n_transactions_month_diff_11	n_transactions_month_diff_12	n_transactions_month_diff_13	balance_month_diff_1	balance_month_diff_2	balance_month_diff_3	balance_month_diff_4	balance_month_diff_5	balance_month_diff_6	balance_month_diff_7	balance_month_diff_8	balance_month_diff_9	balance_month_diff_10	balance_month_diff_11	balance_month_diff_12	balance_month_diff_13	age_bracket	client_age_at_loan	account_age	total_loan_amount_paid	monthly_payment_ratio	region_match	client_district_population_density	account_district_population_density	pre_card_volume_mean	pre_card_volume_std	pre_card_volume_min	pre_card_volume_max	pre_card_volume_sum	pre_card_volume_median	pre_card_credit_mean	pre_card_credit_std	pre_card_credit_min	pre_card_credit_max	pre_card_credit_sum	pre_card_credit_median	pre_card_n_transactions_mean	pre_card_n_transactions_std	pre_card_n_transactions_min	pre_card_n_transactions_max	pre_card_n_transactions_sum	pre_card_n_transactions_median	pre_card_balance_mean	pre_card_balance_std	pre_card_balance_min	pre_card_balance_max	pre_card_balance_sum	pre_card_balance_median
0	1	1	1	18	MONTHLY CHARGES	1995-03-24	18	FEMALE	1970-12-13	29	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-09-18	18	Pisek	south Bohemia	70699	60	13	2	1	4	65.3	8968	2.83	3.35	131	1740	1910	18	Pisek	south Bohemia	70699	60	13	2	1	4	65.3	8968	2.83	3.35	131	1740	1910	False	6492.7	6221.0	6667.1	6212.3	7435.5	7018.6	6701.9	9091.5	10907.2	7318.0	6218.0	6600.6	8282.7	6492.7	6221.0	6667.1	6212.3	7435.5	7018.6	6701.9	9091.5	10907.2	7318.0	6218.0	6600.6	8282.7	5.0	4.0	5.0	4.0	6.0	5.0	5.0	10.0	5.0	5.0	4.0	5.0	6.0	338855.2	332362.5	326141.5	319474.4	313262.1	305826.6	298808.0	292106.1	283014.6	272107.4	264789.4	258571.4	251970.8	[20, 30)	NaN	4	NaN	NaN	True	1.767475e+04	1.767475e+04	8175.802326	2502.492654	1000.0	16298.2	351559.5	7971.10	8175.802326	2502.492654	1000.0	16298.2	351559.5	7971.10	5.255814	1.839941	1	12	226	5.0	181993.216279	104473.501441	1000.0	351559.5	7825708.3	186600.30
1	2	2	2	1	MONTHLY CHARGES	1993-02-26	1	MALE	1945-02-04	54	1.0	1.0	4959.0	1994-01-05	80952.0	24.0	3373.0	contract finished, no problems	NaN	NaN	1993-11-29	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677	99107	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677	99107	False	34617.6	45943.4	52856.1	47098.6	52913.9	37980.7	31345.5	23949.5	1100.0	0.0	0.0	0.0	0.0	34617.6	45943.4	52856.1	47098.6	52913.9	37980.7	31345.5	23949.5	1100.0	0.0	0.0	0.0	0.0	6.0	7.0	7.0	6.0	3.0	3.0	3.0	3.0	1.0	0.0	0.0	0.0	0.0	327805.3	293187.7	247244.3	194388.2	147289.6	94375.7	56395.0	25049.5	1100.0	0.0	0.0	0.0	0.0	[50, 60)	48.0	6	80952.0	0.268958	True	1.204953e+06	1.204953e+06	36456.100000	15568.744255	1100.0	52913.9	364561.0	37368.20	36456.100000	15568.744255	1100.0	52913.9	364561.0	37368.20	4.500000	2.121320	1	7	45	4.5	175139.630000	130344.741006	1100.0	364561.0	1751396.3	170838.90
2	6	6	4	12	MONTHLY CHARGES	1996-02-21	12	MALE	1919-09-22	80	2.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1997-10-26	12	Pribram	central Bohemia	107870	84	29	6	1	6	58.0	8754	3.83	4.31	137	3804	3868	12	Pribram	central Bohemia	107870	84	29	6	1	6	58.0	8754	3.83	4.31	137	3804	3868	False	10917.6	12013.8	9011.7	14254.5	11343.0	9015.0	10769.2	9003.4	20249.4	13529.7	9040.6	9031.2	12329.5	10917.6	12013.8	9011.7	14254.5	11343.0	9015.0	10769.2	9003.4	20249.4	13529.7	9040.6	9031.2	12329.5	6.0	6.0	5.0	6.0	7.0	5.0	6.0	5.0	12.0	7.0	5.0	5.0	6.0	195475.7	184558.1	172544.3	163532.6	149278.1	137935.1	128920.1	118150.9	109147.5	88898.1	75368.4	66327.8	57296.6	[70, 100)	NaN	3	NaN	NaN	True	1.797833e+04	1.797833e+04	9799.609524	4045.715298	800.0	20249.4	205791.8	9040.60	9799.609524	4045.715298	800.0	20249.4	205791.8	9040.60	4.904762	2.681506	1	12	103	5.0	94740.638095	67737.277268	800.0	205791.8	1989553.4	88898.10
3	7	7	5	15	MONTHLY CHARGES	1997-05-30	15	MALE	1929-01-25	70	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-04-27	15	Cesky Krumlov	south Bohemia	58796	22	16	7	1	5	51.9	9045	3.13	3.60	124	1845	1879	15	Cesky Krumlov	south Bohemia	58796	22	16	7	1	5	51.9	9045	3.13	3.60	124	1845	1879	False	8607.1	7797.2	15720.3	10825.1	7812.5	5035.1	5017.0	5017.0	5017.0	5017.0	600.0	0.0	0.0	8607.1	7797.2	15720.3	10825.1	7812.5	5035.1	5017.0	5017.0	5017.0	5017.0	600.0	0.0	0.0	5.0	4.0	10.0	6.0	4.0	3.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	76465.3	67858.2	60061.0	44340.7	33515.6	25703.1	20668.0	15651.0	10634.0	5617.0	600.0	0.0	0.0	[70, 100)	NaN	2	NaN	NaN	True	1.175920e+04	1.175920e+04	7206.491667	3857.313687	600.0	15720.3	86477.9	6416.15	7206.491667	3857.313687	600.0	15720.3	86477.9	6416.15	3.500000	2.779797	1	10	42	3.5	37299.316667	29247.711915	600.0	86477.9	447591.8	29609.35
4	8	8	6	51	MONTHLY CHARGES	1994-09-27	51	FEMALE	1938-02-21	61	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-11-27	51	Trutnov	east Bohemia	121947	37	28	7	3	11	70.5	8541	2.51	2.97	131	3496	3839	51	Trutnov	east Bohemia	121947	37	28	7	3	11	70.5	8541	2.51	2.97	131	3496	3839	False	14333.0	10810.0	10798.3	11989.9	20888.8	13055.1	10807.0	13039.2	10792.4	23857.9	11865.2	10815.6	10803.9	14333.0	10810.0	10798.3	11989.9	20888.8	13055.1	10807.0	13039.2	10792.4	23857.9	11865.2	10815.6	10803.9	5.0	4.0	4.0	5.0	6.0	5.0	4.0	5.0	4.0	11.0	6.0	4.0	4.0	625481.9	611148.9	600338.9	589540.6	577550.7	556661.9	543606.8	532799.8	519760.6	508968.2	485110.3	473245.1	462429.5	[60, 70)	NaN	5	NaN	NaN	True	1.108609e+04	1.108609e+04	12476.490196	4223.409252	900.0	23857.9	636301.0	10974.00	12476.490196	4223.409252	900.0	23857.9	636301.0	10974.00	4.745098	2.133946	1	12	242	5.0	309015.403922	195306.573555	900.0	636301.0	15759785.6	307532.30

pd.set_option('display.max_columns', None) 
client_df.columns

Index(['disp_id', 'client_id', 'account_id', 'account_district_id',
       'account_frequency', 'account_date', 'client_district_id',
       'client_gender', 'client_birth_date', 'client_age',
       ...
       'pre_card_n_transactions_min', 'pre_card_n_transactions_max',
       'pre_card_n_transactions_sum', 'pre_card_n_transactions_median',
       'pre_card_balance_mean', 'pre_card_balance_std', 'pre_card_balance_min',
       'pre_card_balance_max', 'pre_card_balance_sum',
       'pre_card_balance_median'],
      dtype='object', length=160)

Statistical Metrics on the Rollup Window

month_diff_columns = [col for col in client_df.columns if 'month_diff' in col]

prefixes = set(col.rsplit('_', 2)[0] for col in month_diff_columns)

for prefix in prefixes:
    cols = [col for col in month_diff_columns if col.startswith(prefix)]
    client_df[f'{prefix}_mean'] = client_df[cols].mean(axis=1)
    client_df[f'{prefix}_std'] = client_df[cols].std(axis=1)
    client_df[f'{prefix}_min'] = client_df[cols].min(axis=1)
    client_df[f'{prefix}_max'] = client_df[cols].max(axis=1)
    client_df[f'{prefix}_sum'] = client_df[cols].sum(axis=1)
    client_df[f'{prefix}_median'] = client_df[cols].median(axis=1)

client_df.head(5)

	disp_id	client_id	account_id	account_district_id	account_frequency	account_date	client_district_id	client_gender	client_birth_date	client_age	ordertype_HOUSEHOLD	ordertype_LOAN PAYMENT	loan_id	loan_date	loan_amount	loan_duration	loan_payments	loan_status	card_id	card_type	card_issued	district_id_x	client_district_district_name	client_district_region	client_district_population	client_district_n_municipalities_with_inhabitants_lt_499	client_district_n_municipalities_with_inhabitants_500_to_1999	client_district_n_municipalities_with_inhabitants_2000_to_9999	client_district_n_municipalities_with_inhabitants_gt_10000	client_district_n_cities	client_district_ratio_urban_inhabitants	client_district_average_salary	client_district_unemployment_rate_95	client_district_unemployment_rate_96	client_district_enterpreneurs_per_1000_inhabitants	client_district_n_commited_crimes_95	client_district_n_commited_crimes_96	district_id_y	account_district_district_name	account_district_region	account_district_population	account_district_n_municipalities_with_inhabitants_lt_499	account_district_n_municipalities_with_inhabitants_500_to_1999	account_district_n_municipalities_with_inhabitants_2000_to_9999	account_district_n_municipalities_with_inhabitants_gt_10000	account_district_n_cities	account_district_ratio_urban_inhabitants	account_district_average_salary	account_district_unemployment_rate_95	account_district_unemployment_rate_96	account_district_enterpreneurs_per_1000_inhabitants	account_district_n_commited_crimes_95	account_district_n_commited_crimes_96	has_card	volume_month_diff_1	volume_month_diff_2	volume_month_diff_3	volume_month_diff_4	volume_month_diff_5	volume_month_diff_6	volume_month_diff_7	volume_month_diff_8	volume_month_diff_9	volume_month_diff_10	volume_month_diff_11	volume_month_diff_12	volume_month_diff_13	credit_month_diff_1	credit_month_diff_2	credit_month_diff_3	credit_month_diff_4	credit_month_diff_5	credit_month_diff_6	credit_month_diff_7	credit_month_diff_8	credit_month_diff_9	credit_month_diff_10	credit_month_diff_11	credit_month_diff_12	credit_month_diff_13	n_transactions_month_diff_1	n_transactions_month_diff_2	n_transactions_month_diff_3	n_transactions_month_diff_4	n_transactions_month_diff_5	n_transactions_month_diff_6	n_transactions_month_diff_7	n_transactions_month_diff_8	n_transactions_month_diff_9	n_transactions_month_diff_10	n_transactions_month_diff_11	n_transactions_month_diff_12	n_transactions_month_diff_13	balance_month_diff_1	balance_month_diff_2	balance_month_diff_3	balance_month_diff_4	balance_month_diff_5	balance_month_diff_6	balance_month_diff_7	balance_month_diff_8	balance_month_diff_9	balance_month_diff_10	balance_month_diff_11	balance_month_diff_12	balance_month_diff_13	age_bracket	client_age_at_loan	account_age	total_loan_amount_paid	monthly_payment_ratio	region_match	client_district_population_density	account_district_population_density	pre_card_volume_mean	pre_card_volume_std	pre_card_volume_min	pre_card_volume_max	pre_card_volume_sum	pre_card_volume_median	pre_card_credit_mean	pre_card_credit_std	pre_card_credit_min	pre_card_credit_max	pre_card_credit_sum	pre_card_credit_median	pre_card_n_transactions_mean	pre_card_n_transactions_std	pre_card_n_transactions_min	pre_card_n_transactions_max	pre_card_n_transactions_sum	pre_card_n_transactions_median	pre_card_balance_mean	pre_card_balance_std	pre_card_balance_min	pre_card_balance_max	pre_card_balance_sum	pre_card_balance_median	balance_month_mean	balance_month_std	balance_month_min	balance_month_max	balance_month_sum	balance_month_median	volume_month_mean	volume_month_std	volume_month_min	volume_month_max	volume_month_sum	volume_month_median	n_transactions_month_mean	n_transactions_month_std	n_transactions_month_min	n_transactions_month_max	n_transactions_month_sum	n_transactions_month_median	credit_month_mean	credit_month_std	credit_month_min	credit_month_max	credit_month_sum	credit_month_median
0	1	1	1	18	MONTHLY CHARGES	1995-03-24	18	FEMALE	1970-12-13	29	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-09-18	18	Pisek	south Bohemia	70699	60	13	2	1	4	65.3	8968	2.83	3.35	131	1740	1910	18	Pisek	south Bohemia	70699	60	13	2	1	4	65.3	8968	2.83	3.35	131	1740	1910	False	6492.7	6221.0	6667.1	6212.3	7435.5	7018.6	6701.9	9091.5	10907.2	7318.0	6218.0	6600.6	8282.7	6492.7	6221.0	6667.1	6212.3	7435.5	7018.6	6701.9	9091.5	10907.2	7318.0	6218.0	6600.6	8282.7	5.0	4.0	5.0	4.0	6.0	5.0	5.0	10.0	5.0	5.0	4.0	5.0	6.0	338855.2	332362.5	326141.5	319474.4	313262.1	305826.6	298808.0	292106.1	283014.6	272107.4	264789.4	258571.4	251970.8	[20, 30)	NaN	4	NaN	NaN	True	1.767475e+04	1.767475e+04	8175.802326	2502.492654	1000.0	16298.2	351559.5	7971.10	8175.802326	2502.492654	1000.0	16298.2	351559.5	7971.10	5.255814	1.839941	1	12	226	5.0	181993.216279	104473.501441	1000.0	351559.5	7825708.3	186600.30	296714.615385	28986.141531	251970.8	338855.2	3857290.0	298808.0	7320.546154	1375.278204	6212.3	10907.2	95167.1	6701.9	5.307692	1.548366	4.0	10.0	69.0	5.0	7320.546154	1375.278204	6212.3	10907.2	95167.1	6701.9
1	2	2	2	1	MONTHLY CHARGES	1993-02-26	1	MALE	1945-02-04	54	1.0	1.0	4959.0	1994-01-05	80952.0	24.0	3373.0	contract finished, no problems	NaN	NaN	1993-11-29	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677	99107	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677	99107	False	34617.6	45943.4	52856.1	47098.6	52913.9	37980.7	31345.5	23949.5	1100.0	0.0	0.0	0.0	0.0	34617.6	45943.4	52856.1	47098.6	52913.9	37980.7	31345.5	23949.5	1100.0	0.0	0.0	0.0	0.0	6.0	7.0	7.0	6.0	3.0	3.0	3.0	3.0	1.0	0.0	0.0	0.0	0.0	327805.3	293187.7	247244.3	194388.2	147289.6	94375.7	56395.0	25049.5	1100.0	0.0	0.0	0.0	0.0	[50, 60)	48.0	6	80952.0	0.268958	True	1.204953e+06	1.204953e+06	36456.100000	15568.744255	1100.0	52913.9	364561.0	37368.20	36456.100000	15568.744255	1100.0	52913.9	364561.0	37368.20	4.500000	2.121320	1	7	45	4.5	175139.630000	130344.741006	1100.0	364561.0	1751396.3	170838.90	106679.638462	122063.799031	0.0	327805.3	1386835.3	56395.0	25215.792308	22089.010478	0.0	52913.9	327805.3	31345.5	3.000000	2.738613	0.0	7.0	39.0	3.0	25215.792308	22089.010478	0.0	52913.9	327805.3	31345.5
2	6	6	4	12	MONTHLY CHARGES	1996-02-21	12	MALE	1919-09-22	80	2.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1997-10-26	12	Pribram	central Bohemia	107870	84	29	6	1	6	58.0	8754	3.83	4.31	137	3804	3868	12	Pribram	central Bohemia	107870	84	29	6	1	6	58.0	8754	3.83	4.31	137	3804	3868	False	10917.6	12013.8	9011.7	14254.5	11343.0	9015.0	10769.2	9003.4	20249.4	13529.7	9040.6	9031.2	12329.5	10917.6	12013.8	9011.7	14254.5	11343.0	9015.0	10769.2	9003.4	20249.4	13529.7	9040.6	9031.2	12329.5	6.0	6.0	5.0	6.0	7.0	5.0	6.0	5.0	12.0	7.0	5.0	5.0	6.0	195475.7	184558.1	172544.3	163532.6	149278.1	137935.1	128920.1	118150.9	109147.5	88898.1	75368.4	66327.8	57296.6	[70, 100)	NaN	3	NaN	NaN	True	1.797833e+04	1.797833e+04	9799.609524	4045.715298	800.0	20249.4	205791.8	9040.60	9799.609524	4045.715298	800.0	20249.4	205791.8	9040.60	4.904762	2.681506	1	12	103	5.0	94740.638095	67737.277268	800.0	205791.8	1989553.4	88898.10	126725.638462	45719.838797	57296.6	195475.7	1647433.3	128920.1	11577.584615	3172.461770	9003.4	20249.4	150508.6	10917.6	6.230769	1.877669	5.0	12.0	81.0	6.0	11577.584615	3172.461770	9003.4	20249.4	150508.6	10917.6
3	7	7	5	15	MONTHLY CHARGES	1997-05-30	15	MALE	1929-01-25	70	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-04-27	15	Cesky Krumlov	south Bohemia	58796	22	16	7	1	5	51.9	9045	3.13	3.60	124	1845	1879	15	Cesky Krumlov	south Bohemia	58796	22	16	7	1	5	51.9	9045	3.13	3.60	124	1845	1879	False	8607.1	7797.2	15720.3	10825.1	7812.5	5035.1	5017.0	5017.0	5017.0	5017.0	600.0	0.0	0.0	8607.1	7797.2	15720.3	10825.1	7812.5	5035.1	5017.0	5017.0	5017.0	5017.0	600.0	0.0	0.0	5.0	4.0	10.0	6.0	4.0	3.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	76465.3	67858.2	60061.0	44340.7	33515.6	25703.1	20668.0	15651.0	10634.0	5617.0	600.0	0.0	0.0	[70, 100)	NaN	2	NaN	NaN	True	1.175920e+04	1.175920e+04	7206.491667	3857.313687	600.0	15720.3	86477.9	6416.15	7206.491667	3857.313687	600.0	15720.3	86477.9	6416.15	3.500000	2.779797	1	10	42	3.5	37299.316667	29247.711915	600.0	86477.9	447591.8	29609.35	27777.992308	26763.060885	0.0	76465.3	361113.9	20668.0	5881.946154	4442.717706	0.0	15720.3	76465.3	5017.0	2.846154	2.911075	0.0	10.0	37.0	1.0	5881.946154	4442.717706	0.0	15720.3	76465.3	5017.0
4	8	8	6	51	MONTHLY CHARGES	1994-09-27	51	FEMALE	1938-02-21	61	1.0	0.0	NaN	NaT	NaN	NaN	NaN	NaN	NaN	NaN	1998-11-27	51	Trutnov	east Bohemia	121947	37	28	7	3	11	70.5	8541	2.51	2.97	131	3496	3839	51	Trutnov	east Bohemia	121947	37	28	7	3	11	70.5	8541	2.51	2.97	131	3496	3839	False	14333.0	10810.0	10798.3	11989.9	20888.8	13055.1	10807.0	13039.2	10792.4	23857.9	11865.2	10815.6	10803.9	14333.0	10810.0	10798.3	11989.9	20888.8	13055.1	10807.0	13039.2	10792.4	23857.9	11865.2	10815.6	10803.9	5.0	4.0	4.0	5.0	6.0	5.0	4.0	5.0	4.0	11.0	6.0	4.0	4.0	625481.9	611148.9	600338.9	589540.6	577550.7	556661.9	543606.8	532799.8	519760.6	508968.2	485110.3	473245.1	462429.5	[60, 70)	NaN	5	NaN	NaN	True	1.108609e+04	1.108609e+04	12476.490196	4223.409252	900.0	23857.9	636301.0	10974.00	12476.490196	4223.409252	900.0	23857.9	636301.0	10974.00	4.745098	2.133946	1	12	242	5.0	309015.403922	195306.573555	900.0	636301.0	15759785.6	307532.30	545126.400000	53800.670304	462429.5	625481.9	7086643.2	543606.8	13373.561538	4196.460352	10792.4	23857.9	173856.3	11865.2	5.153846	1.908147	4.0	11.0	67.0	5.0	13373.561538	4196.460352	10792.4	23857.9	173856.3	11865.2

First and Last Quarter Rollup Window Statistics

month_diff_columns = [col for col in client_df.columns if 'month_diff' in col]

prefixes = set(col.rsplit('_', 3)[0] for col in month_diff_columns)

for prefix in prefixes:
    first_quarter_cols = [f'{prefix}_month_diff_{i}' for i in range(1, 4)]
    last_quarter_cols = [f'{prefix}_month_diff_{i}' for i in range(10, 13)]
    
    client_df[f'{prefix}_first_quarter_mean'] = client_df[first_quarter_cols].mean(axis=1)
    client_df[f'{prefix}_first_quarter_std'] = client_df[first_quarter_cols].std(axis=1)
    client_df[f'{prefix}_first_quarter_min'] = client_df[first_quarter_cols].min(axis=1)
    client_df[f'{prefix}_first_quarter_max'] = client_df[first_quarter_cols].max(axis=1)
    client_df[f'{prefix}_first_quarter_sum'] = client_df[first_quarter_cols].sum(axis=1)
    client_df[f'{prefix}_first_quarter_median'] = client_df[first_quarter_cols].median(axis=1)
    
    client_df[f'{prefix}_last_quarter_mean'] = client_df[last_quarter_cols].mean(axis=1)
    client_df[f'{prefix}_last_quarter_std'] = client_df[last_quarter_cols].std(axis=1)
    client_df[f'{prefix}_last_quarter_min'] = client_df[last_quarter_cols].min(axis=1)
    client_df[f'{prefix}_last_quarter_max'] = client_df[last_quarter_cols].max(axis=1)
    client_df[f'{prefix}_last_quarter_sum'] = client_df[last_quarter_cols].sum(axis=1)
    client_df[f'{prefix}_last_quarter_median'] = client_df[last_quarter_cols].median(axis=1)

Looking at the effect of the new features

Impact of variance

numerical_cols = client_df.select_dtypes(include=['number']).columns

numerical_variance = client_df[numerical_cols].var()

pd.DataFrame(numerical_variance.sort_values().head(20))

	0
withdrawal_month_diff_2	0.0
withdrawal_month_diff_13	0.0
withdrawal_month_diff_12	0.0
withdrawal_month_diff_11	0.0
withdrawal_month_diff_10	0.0
withdrawal_month_diff_9	0.0
withdrawal_month_diff_8	0.0
pre_card_withdrawal_std	0.0
withdrawal_month_diff_7	0.0
withdrawal_month_diff_5	0.0
withdrawal_month_diff_4	0.0
withdrawal_month_diff_3	0.0
withdrawal_month_diff_1	0.0
pre_card_withdrawal_min	0.0
pre_card_withdrawal_max	0.0
withdrawal_month_diff_6	0.0
withdrawal_last_quarter_median	0.0
withdrawal_last_quarter_sum	0.0
withdrawal_last_quarter_max	0.0
withdrawal_month_sum	0.0

from sklearn.feature_selection import VarianceThreshold
 
vt = VarianceThreshold(threshold=1)
vt.fit(client_df.select_dtypes(include='number'))

low_variance_cols = client_df.select_dtypes(include='number').columns[~vt.get_support()]

client_df.drop(low_variance_cols, axis=1, inplace=True)

Looking at temporal client traits

from src.plot_utils import plot_agg_variables

plot_agg_variables(client_df, 'volume', ['mean'])

Train-Test-Split

from src.train_utils import train_test_split_bal

cleaned_client_df = client_df.loc[:, ~client_df.columns.str.contains('_id|id_|_date|card_issued|age_bracket|card_type')]

X_train, X_test, y_train, y_test = train_test_split_bal(df=cleaned_client_df, target_column='has_card', test_size=0.2, random_state=1337, balancing_technique='undersample')

print('Train set:')
print(y_train.value_counts(normalize=True))
print('\nTest set:')
print(y_test.value_counts(normalize=True))

Train set:
has_card
False    0.5
True     0.5
Name: proportion, dtype: float64

Test set:
has_card
False    0.792732
True     0.207268
Name: proportion, dtype: float64

Baseline Model

Erstellen eines Baseline Modelles mittels logistischer Regression und den Informationen “Alter”, “Geschlecht”, “Domizilregion”, “Vermögen” und “Umsatz” vor Kreditkartenkauf.

from src.plot_utils import (plot_roc_curve, 
                            plot_confusion_matrix, 
                            plot_lift_curve, 
                            plot_precision_recall_curve, 
                            plot_metrics,
                            plot_param_grid_heatmap)

from src.train_utils import (cross_validate,
                             build_preprocessor_pipeline)

from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

baseline_columns = ['client_age', 'client_gender', 'client_district_region']

baseline_preprocessor, baseline_column_selection = build_preprocessor_pipeline(X_train, 
                                                                               include_columns=baseline_columns, 
                                                                               regex_columns='(volume|balance)')

base_pipeline = Pipeline([
    ('preprocessor', baseline_preprocessor),
    ('classifier', LogisticRegression(random_state=1337, max_iter=10_000))
])

(base_estimator, 
 base_params, 
 base_metrics_df, 
 base_roc_curves, 
 base_pr_curves, 
 base_lift_probs, 
 base_true_labels,
 _) = cross_validate(base_pipeline, 
                     baseline_column_selection, 
                     y_train)

model_name = base_estimator.named_steps['classifier'].__class__.__name__

plot_metrics(base_metrics_df, model_name)
plot_confusion_matrix(base_estimator, X_test[baseline_column_selection.columns], y_test, model_name)

Cross-Validation: 100%|██████████| 5/5 [00:00<00:00, 230.63it/s]

base_metrics_df

	Accuracy	Precision	Recall	F1 Score	ROC AUC
0	0.705882	0.706407	0.705882	0.705695	0.782148
1	0.708861	0.709285	0.708861	0.708757	0.781228
2	0.742616	0.743402	0.742616	0.742359	0.814058
3	0.658228	0.658227	0.658228	0.658216	0.708446
4	0.738397	0.740199	0.738397	0.737977	0.831149

Further Model Candidates

Logistic Regression

all_vars_prep_pipeline, all_vars_column_selection  = build_preprocessor_pipeline(X_train, include_columns=X_train.columns.tolist())

lr_pipeline = Pipeline([
    ('preprocessor', all_vars_prep_pipeline),
    ('classifier', LogisticRegression(random_state=1337, max_iter=100_000, solver='saga'))
])

lr_param_grid = {
    'classifier__C': [0.01, 0.1, 1, 10, 100],
    'classifier__penalty': ['l1', 'l2'],
}

(lr_estimator,
 lr_params,
 lr_metrics_df,
 lr_roc_curves,
 lr_pr_curves,
 lr_lift_probs,
 lr_true_labels,
 lr_grid_search) = cross_validate(lr_pipeline, all_vars_column_selection, y_train, param_grid=lr_param_grid)

model_name = lr_estimator.named_steps['classifier'].__class__.__name__
plot_metrics(lr_metrics_df, model_name)
plot_confusion_matrix(lr_estimator, X_test[all_vars_column_selection.columns], y_test, model_name)

Cross-Validation: 100%|██████████| 5/5 [00:00<00:00, 3807.47it/s]

plot_param_grid_heatmap(lr_grid_search)

Decision Tree

from sklearn.tree import DecisionTreeClassifier

dt_pipeline = Pipeline([
    ('preprocessor', all_vars_prep_pipeline),
    ('classifier', DecisionTreeClassifier(random_state=1337))
])

dt_param_grid = {
    'classifier__max_depth': [5, 10, 20, 30, 50],
    'classifier__min_samples_split': [2, 5, 10],
    'classifier__min_samples_leaf': [1, 2, 4]
}

(dt_estimator, 
 dt_params, 
 dt_metrics_df, 
 dt_roc_curves, 
 dt_pr_curves, 
 dt_lift_probs, 
 dt_true_labels,
 dt_grid_search) = cross_validate(pipeline=dt_pipeline, 
                                  X=all_vars_column_selection, 
                                  y=y_train,
                                  param_grid=dt_param_grid)

model_name = dt_estimator.named_steps['classifier'].__class__.__name__
plot_metrics(dt_metrics_df, model_name)
plot_confusion_matrix(dt_estimator, X_test[all_vars_column_selection.columns], y_test, model_name)

Cross-Validation: 100%|██████████| 5/5 [00:00<00:00, 3455.51it/s]

Random Forest

from sklearn.ensemble import RandomForestClassifier

all_vars_prep_pipeline, all_vars_column_selection = build_preprocessor_pipeline(X_train, include_columns=X_train.columns.tolist())

rf_pipeline = Pipeline([
    ('preprocessor', all_vars_prep_pipeline),
    ('classifier', RandomForestClassifier(random_state=1337))
])

rf_param_grid = {
    'classifier__max_depth': [5, 10, 20, 30, 50],
    'classifier__n_estimators': [10, 20, 30]
}

(rf_estimator, 
 rf_params, 
 rf_metrics_df, 
 rf_roc_curves, 
 rf_pr_curves, 
 rf_lift_probs, 
 rf_true_labels,
 rf_grid_search) = cross_validate(rf_pipeline, 
                                  X_train[all_vars_column_selection.columns], 
                                  y_train, 
                                  param_grid=rf_param_grid)

model_name = rf_estimator.named_steps['classifier'].__class__.__name__

plot_metrics(rf_metrics_df, model_name)
plot_confusion_matrix(rf_estimator, X_test[all_vars_column_selection.columns], y_test, model_name)

Cross-Validation: 100%|██████████| 5/5 [00:00<00:00, 3674.06it/s]

Bagging

from sklearn.ensemble import BaggingClassifier

bg_pipeline = Pipeline([
    ('preprocessor', all_vars_prep_pipeline),
    ('classifier', BaggingClassifier(random_state=1337))
])

bg_param_grid = {
    'classifier__n_estimators': [10, 20, 30],
    'classifier__max_features': [10, 20, 30, 40, 50]
}

(bg_estimator, 
 bg_params, 
 bg_metrics_df, 
 bg_roc_curves, 
 bg_pr_curves, 
 bg_lift_probs, 
 bg_true_labels,
 bg_grid_search) = cross_validate(bg_pipeline,
                                  all_vars_column_selection, 
                                  y_train, 
                                  param_grid=bg_param_grid)

model_name = bg_estimator.named_steps['classifier'].__class__.__name__

plot_metrics(bg_metrics_df, model_name)

Cross-Validation: 100%|██████████| 5/5 [00:00<00:00, 1490.30it/s]

Boosting

from sklearn.ensemble import GradientBoostingClassifier

bt_pipeline = Pipeline([
    ('preprocessor', all_vars_prep_pipeline),
    ('classifier', GradientBoostingClassifier(random_state=1337))
])

bt_param_grid = {
    'classifier__n_estimators': [10, 20, 30],
    'classifier__learning_rate': [0.01, 0.1, 1],
    'classifier__max_depth': [5, 10, 20]
}

(bt_estimator, 
 bt_params, 
 bt_metrics_df, 
 bt_roc_curves, 
 bt_pr_curves, 
 bt_lift_probs, 
 bt_true_labels,
 bt_grid_search) = cross_validate(bt_pipeline,
                                  all_vars_column_selection, 
                                  y_train, 
                                  param_grid=bt_param_grid)

model_name = bt_estimator.named_steps['classifier'].__class__.__name__

plot_metrics(bt_metrics_df, model_name)

Cross-Validation: 100%|██████████| 5/5 [00:00<00:00, 3804.70it/s]

Comparing the Model Candidates

ROC Curves

from src.plot_utils import plot_multiple_roc_curves

plot_multiple_roc_curves([base_roc_curves, lr_roc_curves, dt_roc_curves, rf_roc_curves, bg_roc_curves, bt_roc_curves], 
                         ['Baseline Logistic Regression', 'Logistic Regression', 'Decision Tree', 'Random Forest', 'Bagging Classifier', 'Boosting Classifier'])

from src.plot_utils import plot_auc_boxplots

plot_auc_boxplots([base_roc_curves, lr_roc_curves, dt_roc_curves, rf_roc_curves, bg_roc_curves, bt_roc_curves], 
                  ['Baseline Logistic Regression', 'Logistic Regression', 'Decision Tree', 'Random Forest', 'Bagging Classifier', 'Boosting Classifier'])

Confusion Matrices

from src.plot_utils import plot_confusion_matrices

plot_confusion_matrices([base_estimator, lr_estimator, dt_estimator, rf_estimator, bg_estimator, bt_estimator], 
                        X_test[all_vars_column_selection.columns], 
                        y_test,
                        ['Baseline Logistic Regression', 'Logistic Regression', 'Decision Tree', 'Random Forest', 'Bagging Classifier', 'Boosting Classifier'])

Other Metrics

average_metrics = []

for name, df in zip(['Baseline Logistic Regression', 'Logistic Regression', 'Decision Tree', 'Random Forest', 'Bagging Classifier', 'Boosting Classifier'], 
                    [base_metrics_df, lr_metrics_df, dt_metrics_df, rf_metrics_df, bg_metrics_df, bt_metrics_df]):
    mean_metrics = df.mean()
    mean_metrics['Estimator'] = name
    average_metrics.append(mean_metrics)

average_metrics_df = pd.DataFrame(average_metrics)[['Estimator', 'Accuracy', 'Precision', 'Recall', 'F1 Score', 'ROC AUC']]

average_metrics_df

	Estimator	Accuracy	Precision	Recall	F1 Score	ROC AUC
0	Baseline Logistic Regression	0.710797	0.711504	0.710797	0.710601	0.783406
1	Logistic Regression	0.751261	0.751741	0.751261	0.751156	0.831290
2	Decision Tree	0.713357	0.716122	0.713357	0.712285	0.761376
3	Random Forest	0.740322	0.748519	0.740322	0.738179	0.814467
4	Bagging Classifier	0.748729	0.751649	0.748729	0.747976	0.818094
5	Boosting Classifier	0.745346	0.746812	0.745346	0.744910	0.820061

Model Reduction

Selecting Features

lr_rfe_pipeline = Pipeline([
    ('preprocessor', all_vars_prep_pipeline),
    ('classifier', LogisticRegression(**lr_estimator.named_steps['classifier'].get_params()))
])

(lr_rfe_estimator,
 lr_rfe_params,
 lr_rfe_metrics_df,
 lr_rfe_roc_curves,
 lr_rfe_pr_curves,
 lr_rfe_lift_probs,
 lr_rfe_true_labels,
 _) = cross_validate(lr_rfe_pipeline, all_vars_column_selection, y_train, n_features_to_select=20)

Cross-Validation: 100%|██████████| 5/5 [00:00<00:00, 738.43it/s]

rfe_step = lr_rfe_estimator.named_steps['rfe']
column_ids = rfe_step.get_support()

rfe_selected_columns = lr_rfe_estimator.named_steps['preprocessor'].get_feature_names_out()[column_ids]

pd.DataFrame(rfe_selected_columns, columns=['Selected Features'])

	Selected Features
0	num__client_age
1	num__client_district_n_municipalities_with_inh...
2	num__volume_month_diff_4
3	num__credit_month_diff_4
4	num__n_transactions_month_diff_1
5	num__n_transactions_month_diff_13
6	num__account_age
7	num__region_match
8	num__pre_card_volume_median
9	num__pre_card_credit_min
10	num__pre_card_credit_median
11	num__pre_card_n_transactions_sum
12	num__pre_card_n_transactions_median
13	num__n_transactions_month_min
14	num__n_transactions_month_max
15	num__balance_first_quarter_std
16	num__n_transactions_first_quarter_std
17	num__volume_first_quarter_min
18	num__credit_first_quarter_min
19	cat__account_frequency_MONTHLY CHARGES

Model Explanation

from dalex import Explainer

X_train_rfe_prep = lr_rfe_estimator.named_steps['preprocessor'].transform(X_train[all_vars_column_selection.columns])
X_train_rfe_selected_cols = lr_rfe_estimator.named_steps['rfe'].transform(X_train_rfe_prep)

X_train_rfe_selected_cols = X_train_rfe_selected_cols.toarray()

X_train_rfe_selected_cols = pd.DataFrame(X_train_rfe_selected_cols, columns=rfe_selected_columns)

explainer = Explainer(lr_rfe_estimator.named_steps['classifier'], X_train_rfe_selected_cols, y_train, label='Reduced Logistic Regression')

Preparation of a new explainer is initiated

  -> data              : 1186 rows 20 cols
  -> target variable   : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
  -> target variable   : 1186 values
  -> model_class       : sklearn.linear_model._logistic.LogisticRegression (default)
  -> label             : Reduced Logistic Regression
  -> predict function  : <function yhat_proba_default at 0x17ee2e700> will be used (default)
  -> predict function  : Accepts pandas.DataFrame and numpy.ndarray.
  -> predicted values  : min = 0.023, mean = 0.5, max = 0.994
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -0.994, mean = -0.000496, max = 0.944
  -> model_info        : package sklearn

A new explainer has been created!

explainer.model_performance()

	recall	precision	f1	accuracy	auc
Reduced Logistic Regression	0.767285	0.775128	0.771186	0.772344	0.853325

explainer.model_parts(type='variable_importance').plot()

pd_rf = explainer.model_profile()

pd_rf.plot()

Calculating ceteris paribus: 100%|██████████| 20/20 [00:00<00:00, 77.48it/s]

Top Customer Lists

from src.plot_utils import plot_model_concordance

estimators = [base_estimator, lr_estimator, dt_estimator, rf_estimator, bg_estimator]
estimator_names = ['Baseline Logistic Regression', 'Logistic Regression', 'Decision Tree', 'Random Forest', 'Bagging Classifier']
non_card_holders = cleaned_client_df[cleaned_client_df['has_card'] == False].drop(columns='has_card')

plot_model_concordance(estimators, estimator_names, non_card_holders, p=0.05)

plot_model_concordance(estimators, estimator_names, non_card_holders, p=0.1)

plot_lift_curve(lr_rfe_lift_probs, lr_rfe_true_labels, 'Logistic Regression with RFE')